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Type I Spurious Regression in Econometrics

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Abstract

In applied econometrics researchers often infer the relation among nonstationary time series by regression of their differences. The aim of this paper is to show that in some circumstances regression of differenced time series tends to reject the relation among their levels. This phenomenon is known as type I spurious regression. Time series are dynamic processes, and the ignored system dynamics will become the systematic errors in regression equations. Differencing does not preserve the underlying relation among time series in regression due to systematic errors. This paper will outline how regression of differenced time series tends to reject the relation between their levels, and so potentially to incur type I spurious regression.

Suggested Citation

  • Carl Chiarella & Shenhuai Gao, 2002. "Type I Spurious Regression in Econometrics," Working Paper Series 114, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    • Handle: RePEc:uts:wpaper:114
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    File URL: http://www.finance.uts.edu.au/research/wpapers/wp114.pdf
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    References listed on IDEAS

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    1. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    2. Carl Chiarella & Shenhuai Gao, 2002. "Solving the Price-Earnings Puzzle," Working Paper Series 116, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    3. Nelson, Charles R. & Plosser, Charles I., 1982. "Trends and random walks in macroeconmic time series : Some evidence and implications," Journal of Monetary Economics, Elsevier, vol. 10(2), pages 139-162.
    4. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
    5. Harvey, Andrew, 1997. "Trends, Cycles and Autoregressions," Economic Journal, Royal Economic Society, vol. 107(440), pages 192-201, January.
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    Citations

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    Cited by:

    1. Ivan O. KITOV & Oleg I. KITOV & Svetlana A. DOLINSKAYA, 2009. "Modelling Real Gdp Per Capita In The Usa:Cointegration Tests," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 4(1(7)_ Spr).
    2. Li, Jing, 2012. "Economic segregation and urban growth," MPRA Paper 41050, University Library of Munich, Germany.
    3. Oleg KITOV & Ivan KITOV, 2012. "A Win-Win Monetary Policy In Canada," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 6(6(18)/ Su), pages 160-176.
    4. Kitov, Ivan & Kitov, Oleg & Dolinskaya, Svetlana, 2007. "Relationship between inflation, unemployment and labor force change rate in France: cointegration test," MPRA Paper 2736, University Library of Munich, Germany.
    5. Stanova, Nadja, 2015. "Effects of fiscal shocks in new EU members estimated from a SVARX model with debt feedback," MPRA Paper 63148, University Library of Munich, Germany.
    6. Carl Chiarella & Shenhuai Gao, 2002. "Modelling the Value of the S&P 500 - A System Dynamics Perspective," Working Paper Series 115, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    7. Carl Chiarella & Shenhuai Gao, 2002. "Solving the Price-Earnings Puzzle," Working Paper Series 116, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    8. Ai Deng, 2014. "Understanding Spurious Regression in Financial Economics," Journal of Financial Econometrics, Oxford University Press, vol. 12(1), pages 122-150.
    9. Kitov, Ivan & Kitov, Oleg & Dolinskaya, Svetlana, 2007. "Inflation as a function of labor force change rate: cointegration test for the USA," MPRA Paper 2734, University Library of Munich, Germany.
    10. Ivan Kitov & Oleg Kitov & Svetlana Dolinskaya, 2007. "Linear Lagged Relationship Between Inflation, Unemployment and Labor Force Change Rate in France: Cointegration Test," Mechonomics mechonomics2, Socionet.
    11. Carl Chiarella & Shenhuai Gao, 2004. "Continuous Time Model Estimation," Working Paper Series 138, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    12. de Mendonça, Helder Ferreira & da Silva Veiga, Igor, 2014. "A Note On Openness And Inflation Targeting: Implications For The Unpleasant Fiscal Arithmetic," Macroeconomic Dynamics, Cambridge University Press, vol. 18(5), pages 1187-1207, July.

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    More about this item

    Keywords

    type I spurious regression; systematic errors; invariant dynamic relations;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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